Overview

Dataset statistics

Number of variables60
Number of observations28800
Missing cells189575
Missing cells (%)11.0%
Total size in memory13.2 MiB
Average record size in memory480.0 B

Variable types

Numeric25
Text35

Alerts

psych_disturb has 2062 (7.2%) missing valuesMissing
cyto_score has 8068 (28.0%) missing valuesMissing
diabetes has 2119 (7.4%) missing valuesMissing
hla_match_c_high has 4620 (16.0%) missing valuesMissing
hla_high_res_8 has 5829 (20.2%) missing valuesMissing
arrhythmia has 2202 (7.6%) missing valuesMissing
hla_low_res_6 has 3270 (11.4%) missing valuesMissing
renal_issue has 1915 (6.6%) missing valuesMissing
pulm_severe has 2135 (7.4%) missing valuesMissing
hla_high_res_6 has 5284 (18.3%) missing valuesMissing
cmv_status has 634 (2.2%) missing valuesMissing
hla_high_res_10 has 7163 (24.9%) missing valuesMissing
hla_match_dqb1_high has 5199 (18.1%) missing valuesMissing
tce_imm_match has 11133 (38.7%) missing valuesMissing
hla_nmdp_6 has 4197 (14.6%) missing valuesMissing
hla_match_c_low has 2800 (9.7%) missing valuesMissing
rituximab has 2148 (7.5%) missing valuesMissing
hla_match_drb1_low has 2643 (9.2%) missing valuesMissing
hla_match_dqb1_low has 4194 (14.6%) missing valuesMissing
cyto_score_detail has 11923 (41.4%) missing valuesMissing
conditioning_intensity has 4789 (16.6%) missing valuesMissing
ethnicity has 587 (2.0%) missing valuesMissing
obesity has 1760 (6.1%) missing valuesMissing
mrd_hct has 16597 (57.6%) missing valuesMissing
tce_match has 18996 (66.0%) missing valuesMissing
hla_match_a_high has 4301 (14.9%) missing valuesMissing
hepatic_severe has 1871 (6.5%) missing valuesMissing
donor_age has 1808 (6.3%) missing valuesMissing
prior_tumor has 1678 (5.8%) missing valuesMissing
hla_match_b_low has 2565 (8.9%) missing valuesMissing
peptic_ulcer has 2419 (8.4%) missing valuesMissing
hla_match_a_low has 2390 (8.3%) missing valuesMissing
rheum_issue has 2183 (7.6%) missing valuesMissing
hla_match_b_high has 4088 (14.2%) missing valuesMissing
comorbidity_score has 477 (1.7%) missing valuesMissing
karnofsky_score has 870 (3.0%) missing valuesMissing
hepatic_mild has 1917 (6.7%) missing valuesMissing
tce_div_match has 11396 (39.6%) missing valuesMissing
melphalan_dose has 1405 (4.9%) missing valuesMissing
hla_low_res_8 has 3653 (12.7%) missing valuesMissing
cardiac has 2542 (8.8%) missing valuesMissing
hla_match_drb1_high has 3352 (11.6%) missing valuesMissing
pulm_moderate has 2047 (7.1%) missing valuesMissing
hla_low_res_10 has 5064 (17.6%) missing valuesMissing
ID has unique valuesUnique
comorbidity_score has 10738 (37.3%) zerosZeros
efs has 13268 (46.1%) zerosZeros

Reproduction

Analysis started2024-12-17 11:01:27.608879
Analysis finished2024-12-17 11:01:28.353244
Duration0.74 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct28800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14399.5
Minimum0
Maximum28799
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:28.454561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1439.95
Q17199.75
median14399.5
Q321599.25
95-th percentile27359.05
Maximum28799
Range28799
Interquartile range (IQR)14399.5

Descriptive statistics

Standard deviation8313.988213
Coefficient of variation (CV)0.5773803405
Kurtosis-1.2
Mean14399.5
Median Absolute Deviation (MAD)7200
Skewness0
Sum414705600
Variance69122400
MonotonicityStrictly increasing
2024-12-17T12:01:28.587592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
19237 1
 
< 0.1%
19207 1
 
< 0.1%
19206 1
 
< 0.1%
19205 1
 
< 0.1%
19204 1
 
< 0.1%
19203 1
 
< 0.1%
19202 1
 
< 0.1%
19201 1
 
< 0.1%
19200 1
 
< 0.1%
Other values (28790) 28790
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
28799 1
< 0.1%
28798 1
< 0.1%
28797 1
< 0.1%
28796 1
< 0.1%
28795 1
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing154
Missing (%)0.5%
Memory size225.1 KiB
2024-12-17T12:01:28.709898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length14.59331146
Min length3

Characters and Unicode

Total characters418040
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN/A - non-malignant indication
2nd rowIntermediate
3rd rowN/A - non-malignant indication
4th rowHigh
5th rowHigh
ValueCountFrequency (%)
intermediate 10917
18.1%
9373
15.6%
n/a 7478
12.4%
high 6313
10.5%
pediatric 4779
7.9%
cytogenetics 3898
 
6.5%
non-malignant 2427
 
4.0%
indication 2427
 
4.0%
tbd 2003
 
3.3%
low 1926
 
3.2%
Other values (9) 8621
14.3%
2024-12-17T12:01:28.945360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 48253
 
11.5%
i 45027
 
10.8%
t 39553
 
9.5%
n 31553
 
7.5%
31516
 
7.5%
a 25706
 
6.1%
d 18404
 
4.4%
c 17169
 
4.1%
r 15894
 
3.8%
m 15239
 
3.6%
Other values (25) 129726
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 48253
 
11.5%
i 45027
 
10.8%
t 39553
 
9.5%
n 31553
 
7.5%
31516
 
7.5%
a 25706
 
6.1%
d 18404
 
4.4%
c 17169
 
4.1%
r 15894
 
3.8%
m 15239
 
3.6%
Other values (25) 129726
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 48253
 
11.5%
i 45027
 
10.8%
t 39553
 
9.5%
n 31553
 
7.5%
31516
 
7.5%
a 25706
 
6.1%
d 18404
 
4.4%
c 17169
 
4.1%
r 15894
 
3.8%
m 15239
 
3.6%
Other values (25) 129726
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 48253
 
11.5%
i 45027
 
10.8%
t 39553
 
9.5%
n 31553
 
7.5%
31516
 
7.5%
a 25706
 
6.1%
d 18404
 
4.4%
c 17169
 
4.1%
r 15894
 
3.8%
m 15239
 
3.6%
Other values (25) 129726
31.0%

psych_disturb
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2062
Missing (%)7.2%
Memory size225.1 KiB
2024-12-17T12:01:29.028174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.166916
Min length2

Characters and Unicode

Total characters57939
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 23005
85.6%
yes 3587
 
13.3%
not 146
 
0.5%
done 146
 
0.5%
2024-12-17T12:01:29.307508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 23297
40.2%
N 23151
40.0%
e 3733
 
6.4%
Y 3587
 
6.2%
s 3587
 
6.2%
t 146
 
0.3%
146
 
0.3%
d 146
 
0.3%
n 146
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 23297
40.2%
N 23151
40.0%
e 3733
 
6.4%
Y 3587
 
6.2%
s 3587
 
6.2%
t 146
 
0.3%
146
 
0.3%
d 146
 
0.3%
n 146
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 23297
40.2%
N 23151
40.0%
e 3733
 
6.4%
Y 3587
 
6.2%
s 3587
 
6.2%
t 146
 
0.3%
146
 
0.3%
d 146
 
0.3%
n 146
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 23297
40.2%
N 23151
40.0%
e 3733
 
6.4%
Y 3587
 
6.2%
s 3587
 
6.2%
t 146
 
0.3%
146
 
0.3%
d 146
 
0.3%
n 146
 
0.3%

cyto_score
Text

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing8068
Missing (%)28.0%
Memory size225.1 KiB
2024-12-17T12:01:29.399883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.224098013
Min length3

Characters and Unicode

Total characters149770
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermediate
2nd rowIntermediate
3rd rowPoor
4th rowPoor
5th rowOther
ValueCountFrequency (%)
poor 8802
42.3%
intermediate 6376
30.7%
favorable 3011
 
14.5%
tbd 1341
 
6.5%
normal 643
 
3.1%
other 504
 
2.4%
not 55
 
0.3%
tested 55
 
0.3%
2024-12-17T12:01:29.587424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22753
15.2%
o 21313
14.2%
r 19336
12.9%
t 13421
9.0%
a 13041
8.7%
P 8802
 
5.9%
m 7019
 
4.7%
d 6431
 
4.3%
I 6376
 
4.3%
n 6376
 
4.3%
Other values (13) 24902
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22753
15.2%
o 21313
14.2%
r 19336
12.9%
t 13421
9.0%
a 13041
8.7%
P 8802
 
5.9%
m 7019
 
4.7%
d 6431
 
4.3%
I 6376
 
4.3%
n 6376
 
4.3%
Other values (13) 24902
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22753
15.2%
o 21313
14.2%
r 19336
12.9%
t 13421
9.0%
a 13041
8.7%
P 8802
 
5.9%
m 7019
 
4.7%
d 6431
 
4.3%
I 6376
 
4.3%
n 6376
 
4.3%
Other values (13) 24902
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22753
15.2%
o 21313
14.2%
r 19336
12.9%
t 13421
9.0%
a 13041
8.7%
P 8802
 
5.9%
m 7019
 
4.7%
d 6431
 
4.3%
I 6376
 
4.3%
n 6376
 
4.3%
Other values (13) 24902
16.6%

diabetes
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2119
Missing (%)7.4%
Memory size225.1 KiB
2024-12-17T12:01:29.661063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.194333046
Min length2

Characters and Unicode

Total characters58547
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 22201
82.8%
yes 4339
 
16.2%
not 141
 
0.5%
done 141
 
0.5%
2024-12-17T12:01:29.832311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 22483
38.4%
N 22342
38.2%
e 4480
 
7.7%
Y 4339
 
7.4%
s 4339
 
7.4%
t 141
 
0.2%
141
 
0.2%
d 141
 
0.2%
n 141
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 22483
38.4%
N 22342
38.2%
e 4480
 
7.7%
Y 4339
 
7.4%
s 4339
 
7.4%
t 141
 
0.2%
141
 
0.2%
d 141
 
0.2%
n 141
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 22483
38.4%
N 22342
38.2%
e 4480
 
7.7%
Y 4339
 
7.4%
s 4339
 
7.4%
t 141
 
0.2%
141
 
0.2%
d 141
 
0.2%
n 141
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 22483
38.4%
N 22342
38.2%
e 4480
 
7.7%
Y 4339
 
7.4%
s 4339
 
7.4%
t 141
 
0.2%
141
 
0.2%
d 141
 
0.2%
n 141
 
0.2%

hla_match_c_high
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4620
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean1.764516129
Minimum0
Maximum2
Zeros79
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:29.927645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4319412684
Coefficient of variation (CV)0.2447930406
Kurtosis0.2251654307
Mean1.764516129
Median Absolute Deviation (MAD)0
Skewness-1.367968521
Sum42666
Variance0.1865732594
MonotonicityNot monotonic
2024-12-17T12:01:30.019661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 18565
64.5%
1 5536
 
19.2%
0 79
 
0.3%
(Missing) 4620
 
16.0%
ValueCountFrequency (%)
0 79
 
0.3%
1 5536
 
19.2%
2 18565
64.5%
ValueCountFrequency (%)
2 18565
64.5%
1 5536
 
19.2%
0 79
 
0.3%

hla_high_res_8
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing5829
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean6.876801184
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:30.109496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median8
Q38
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.56431343
Coefficient of variation (CV)0.2274769021
Kurtosis-0.7312498783
Mean6.876801184
Median Absolute Deviation (MAD)0
Skewness-0.972462785
Sum157967
Variance2.447076506
MonotonicityNot monotonic
2024-12-17T12:01:30.196501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 13568
47.1%
4 3820
 
13.3%
7 2385
 
8.3%
5 1648
 
5.7%
6 1520
 
5.3%
3 28
 
0.1%
2 2
 
< 0.1%
(Missing) 5829
20.2%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 28
 
0.1%
4 3820
13.3%
5 1648
5.7%
6 1520
 
5.3%
ValueCountFrequency (%)
8 13568
47.1%
7 2385
 
8.3%
6 1520
 
5.3%
5 1648
 
5.7%
4 3820
 
13.3%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:30.275941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length6
Mean length10.14385417
Min length6

Characters and Unicode

Total characters292143
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo TBI
2nd rowTBI +- Other, >cGy
3rd rowNo TBI
4th rowNo TBI
5th rowNo TBI
ValueCountFrequency (%)
tbi 28800
34.3%
no 18861
22.4%
16043
19.1%
other 9939
 
11.8%
cy 6104
 
7.3%
cgy 3759
 
4.5%
unknown 155
 
0.2%
dose 155
 
0.2%
single 134
 
0.2%
fractionated 119
 
0.1%
2024-12-17T12:01:30.466377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55269
18.9%
T 28800
9.9%
B 28800
9.9%
I 28800
9.9%
o 19290
 
6.6%
N 18861
 
6.5%
+ 16043
 
5.5%
e 10347
 
3.5%
- 10271
 
3.5%
t 10177
 
3.5%
Other values (22) 65485
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
55269
18.9%
T 28800
9.9%
B 28800
9.9%
I 28800
9.9%
o 19290
 
6.6%
N 18861
 
6.5%
+ 16043
 
5.5%
e 10347
 
3.5%
- 10271
 
3.5%
t 10177
 
3.5%
Other values (22) 65485
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
55269
18.9%
T 28800
9.9%
B 28800
9.9%
I 28800
9.9%
o 19290
 
6.6%
N 18861
 
6.5%
+ 16043
 
5.5%
e 10347
 
3.5%
- 10271
 
3.5%
t 10177
 
3.5%
Other values (22) 65485
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
55269
18.9%
T 28800
9.9%
B 28800
9.9%
I 28800
9.9%
o 19290
 
6.6%
N 18861
 
6.5%
+ 16043
 
5.5%
e 10347
 
3.5%
- 10271
 
3.5%
t 10177
 
3.5%
Other values (22) 65485
22.4%

arrhythmia
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2202
Missing (%)7.6%
Memory size225.1 KiB
2024-12-17T12:01:30.536969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.074629671
Min length2

Characters and Unicode

Total characters55181
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 25203
94.3%
yes 1277
 
4.8%
not 118
 
0.4%
done 118
 
0.4%
2024-12-17T12:01:30.707717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 25439
46.1%
N 25321
45.9%
e 1395
 
2.5%
Y 1277
 
2.3%
s 1277
 
2.3%
t 118
 
0.2%
118
 
0.2%
d 118
 
0.2%
n 118
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25439
46.1%
N 25321
45.9%
e 1395
 
2.5%
Y 1277
 
2.3%
s 1277
 
2.3%
t 118
 
0.2%
118
 
0.2%
d 118
 
0.2%
n 118
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25439
46.1%
N 25321
45.9%
e 1395
 
2.5%
Y 1277
 
2.3%
s 1277
 
2.3%
t 118
 
0.2%
118
 
0.2%
d 118
 
0.2%
n 118
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25439
46.1%
N 25321
45.9%
e 1395
 
2.5%
Y 1277
 
2.3%
s 1277
 
2.3%
t 118
 
0.2%
118
 
0.2%
d 118
 
0.2%
n 118
 
0.2%

hla_low_res_6
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3270
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean5.143321582
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:30.804032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q36
95-th percentile6
Maximum6
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.207757356
Coefficient of variation (CV)0.234820502
Kurtosis-0.8150148556
Mean5.143321582
Median Absolute Deviation (MAD)0
Skewness-0.9491632192
Sum131309
Variance1.45867783
MonotonicityNot monotonic
2024-12-17T12:01:30.898180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
6 15690
54.5%
3 4955
 
17.2%
5 2808
 
9.8%
4 2055
 
7.1%
2 22
 
0.1%
(Missing) 3270
 
11.4%
ValueCountFrequency (%)
2 22
 
0.1%
3 4955
 
17.2%
4 2055
 
7.1%
5 2808
 
9.8%
6 15690
54.5%
ValueCountFrequency (%)
6 15690
54.5%
5 2808
 
9.8%
4 2055
 
7.1%
3 4955
 
17.2%
2 22
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:30.982222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length14.56701389
Min length11

Characters and Unicode

Total characters419530
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBone marrow
2nd rowPeripheral blood
3rd rowBone marrow
4th rowBone marrow
5th rowPeripheral blood
ValueCountFrequency (%)
peripheral 20546
35.7%
blood 20546
35.7%
bone 8254
14.3%
marrow 8254
14.3%
2024-12-17T12:01:31.165674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 57600
13.7%
o 57600
13.7%
e 49346
11.8%
l 41092
9.8%
a 28800
 
6.9%
28800
 
6.9%
P 20546
 
4.9%
i 20546
 
4.9%
p 20546
 
4.9%
h 20546
 
4.9%
Other values (6) 74108
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 419530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 57600
13.7%
o 57600
13.7%
e 49346
11.8%
l 41092
9.8%
a 28800
 
6.9%
28800
 
6.9%
P 20546
 
4.9%
i 20546
 
4.9%
p 20546
 
4.9%
h 20546
 
4.9%
Other values (6) 74108
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 419530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 57600
13.7%
o 57600
13.7%
e 49346
11.8%
l 41092
9.8%
a 28800
 
6.9%
28800
 
6.9%
P 20546
 
4.9%
i 20546
 
4.9%
p 20546
 
4.9%
h 20546
 
4.9%
Other values (6) 74108
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 419530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 57600
13.7%
o 57600
13.7%
e 49346
11.8%
l 41092
9.8%
a 28800
 
6.9%
28800
 
6.9%
P 20546
 
4.9%
i 20546
 
4.9%
p 20546
 
4.9%
h 20546
 
4.9%
Other values (6) 74108
17.7%
Distinct2
Distinct (%)< 0.1%
Missing259
Missing (%)0.9%
Memory size225.1 KiB
2024-12-17T12:01:31.234273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.028730598
Min length2

Characters and Unicode

Total characters57902
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 27721
97.1%
yes 820
 
2.9%
2024-12-17T12:01:31.429283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 27721
47.9%
o 27721
47.9%
Y 820
 
1.4%
e 820
 
1.4%
s 820
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 27721
47.9%
o 27721
47.9%
Y 820
 
1.4%
e 820
 
1.4%
s 820
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 27721
47.9%
o 27721
47.9%
Y 820
 
1.4%
e 820
 
1.4%
s 820
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 27721
47.9%
o 27721
47.9%
Y 820
 
1.4%
e 820
 
1.4%
s 820
 
1.4%

renal_issue
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1915
Missing (%)6.6%
Memory size225.1 KiB
2024-12-17T12:01:31.498498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.038013762
Min length2

Characters and Unicode

Total characters54792
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 26548
98.2%
yes 200
 
0.7%
not 137
 
0.5%
done 137
 
0.5%
2024-12-17T12:01:31.669908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 26822
49.0%
N 26685
48.7%
e 337
 
0.6%
Y 200
 
0.4%
s 200
 
0.4%
t 137
 
0.3%
137
 
0.3%
d 137
 
0.3%
n 137
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26822
49.0%
N 26685
48.7%
e 337
 
0.6%
Y 200
 
0.4%
s 200
 
0.4%
t 137
 
0.3%
137
 
0.3%
d 137
 
0.3%
n 137
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26822
49.0%
N 26685
48.7%
e 337
 
0.6%
Y 200
 
0.4%
s 200
 
0.4%
t 137
 
0.3%
137
 
0.3%
d 137
 
0.3%
n 137
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26822
49.0%
N 26685
48.7%
e 337
 
0.6%
Y 200
 
0.4%
s 200
 
0.4%
t 137
 
0.3%
137
 
0.3%
d 137
 
0.3%
n 137
 
0.3%

pulm_severe
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2135
Missing (%)7.4%
Memory size225.1 KiB
2024-12-17T12:01:31.740153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.10448153
Min length2

Characters and Unicode

Total characters56116
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 24779
92.3%
yes 1706
 
6.4%
not 180
 
0.7%
done 180
 
0.7%
2024-12-17T12:01:31.911248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 25139
44.8%
N 24959
44.5%
e 1886
 
3.4%
Y 1706
 
3.0%
s 1706
 
3.0%
t 180
 
0.3%
180
 
0.3%
d 180
 
0.3%
n 180
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25139
44.8%
N 24959
44.5%
e 1886
 
3.4%
Y 1706
 
3.0%
s 1706
 
3.0%
t 180
 
0.3%
180
 
0.3%
d 180
 
0.3%
n 180
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25139
44.8%
N 24959
44.5%
e 1886
 
3.4%
Y 1706
 
3.0%
s 1706
 
3.0%
t 180
 
0.3%
180
 
0.3%
d 180
 
0.3%
n 180
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25139
44.8%
N 24959
44.5%
e 1886
 
3.4%
Y 1706
 
3.0%
s 1706
 
3.0%
t 180
 
0.3%
180
 
0.3%
d 180
 
0.3%
n 180
 
0.3%
Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:32.008582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.228819444
Min length2

Characters and Unicode

Total characters92990
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIEA
2nd rowAML
3rd rowHIS
4th rowALL
5th rowMPN
ValueCountFrequency (%)
all 8102
27.4%
aml 7135
24.2%
mds 3046
 
10.3%
ipa 1719
 
5.8%
mpn 1656
 
5.6%
iea 1449
 
4.9%
nhl 1319
 
4.5%
iis 1024
 
3.5%
pcd 869
 
2.9%
saa 713
 
2.4%
Other values (10) 2507
 
8.5%
2024-12-17T12:01:32.213142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 24678
26.5%
A 20280
21.8%
M 12001
12.9%
I 6254
 
6.7%
S 5435
 
5.8%
P 4244
 
4.6%
D 4113
 
4.4%
N 2975
 
3.2%
H 1818
 
2.0%
E 1449
 
1.6%
Other values (16) 9743
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 24678
26.5%
A 20280
21.8%
M 12001
12.9%
I 6254
 
6.7%
S 5435
 
5.8%
P 4244
 
4.6%
D 4113
 
4.4%
N 2975
 
3.2%
H 1818
 
2.0%
E 1449
 
1.6%
Other values (16) 9743
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 24678
26.5%
A 20280
21.8%
M 12001
12.9%
I 6254
 
6.7%
S 5435
 
5.8%
P 4244
 
4.6%
D 4113
 
4.4%
N 2975
 
3.2%
H 1818
 
2.0%
E 1449
 
1.6%
Other values (16) 9743
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 24678
26.5%
A 20280
21.8%
M 12001
12.9%
I 6254
 
6.7%
S 5435
 
5.8%
P 4244
 
4.6%
D 4113
 
4.4%
N 2975
 
3.2%
H 1818
 
2.0%
E 1449
 
1.6%
Other values (16) 9743
 
10.5%

hla_high_res_6
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing5284
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean5.109202245
Minimum0
Maximum6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:32.310305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.214162101
Coefficient of variation (CV)0.2376422077
Kurtosis-0.8921409576
Mean5.109202245
Median Absolute Deviation (MAD)0
Skewness-0.8921556299
Sum120148
Variance1.474189608
MonotonicityNot monotonic
2024-12-17T12:01:32.395694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 14022
48.7%
3 4596
 
16.0%
5 2726
 
9.5%
4 2128
 
7.4%
2 43
 
0.1%
0 1
 
< 0.1%
(Missing) 5284
 
18.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 43
 
0.1%
3 4596
16.0%
4 2128
7.4%
5 2726
9.5%
ValueCountFrequency (%)
6 14022
48.7%
5 2726
 
9.5%
4 2128
 
7.4%
3 4596
 
16.0%
2 43
 
0.1%

cmv_status
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing634
Missing (%)2.2%
Memory size225.1 KiB
2024-12-17T12:01:32.451196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84498
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+/+
2nd row+/+
3rd row+/+
4th row+/+
5th row+/+
ValueCountFrequency (%)
28166
100.0%
2024-12-17T12:01:32.601909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 38321
45.4%
/ 28166
33.3%
- 18011
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
+ 38321
45.4%
/ 28166
33.3%
- 18011
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
+ 38321
45.4%
/ 28166
33.3%
- 18011
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
+ 38321
45.4%
/ 28166
33.3%
- 18011
21.3%

hla_high_res_10
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing7163
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean8.617229745
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:32.695435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q17
median10
Q310
95-th percentile10
Maximum10
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.905125089
Coefficient of variation (CV)0.221083242
Kurtosis-0.6485622338
Mean8.617229745
Median Absolute Deviation (MAD)0
Skewness-0.9984792734
Sum186451
Variance3.629501606
MonotonicityNot monotonic
2024-12-17T12:01:32.784441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 12232
42.5%
5 3161
 
11.0%
9 2369
 
8.2%
6 1355
 
4.7%
8 1314
 
4.6%
7 1180
 
4.1%
4 25
 
0.1%
3 1
 
< 0.1%
(Missing) 7163
24.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 25
 
0.1%
5 3161
11.0%
6 1355
4.7%
7 1180
 
4.1%
ValueCountFrequency (%)
10 12232
42.5%
9 2369
 
8.2%
8 1314
 
4.6%
7 1180
 
4.1%
6 1355
 
4.7%

hla_match_dqb1_high
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing5199
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean1.736875556
Minimum0
Maximum2
Zeros77
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:32.872828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4476869557
Coefficient of variation (CV)0.2577541921
Kurtosis-0.2887347568
Mean1.736875556
Median Absolute Deviation (MAD)0
Skewness-1.18463475
Sum40992
Variance0.2004236103
MonotonicityNot monotonic
2024-12-17T12:01:32.965401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 17468
60.7%
1 6056
 
21.0%
0 77
 
0.3%
(Missing) 5199
 
18.1%
ValueCountFrequency (%)
0 77
 
0.3%
1 6056
 
21.0%
2 17468
60.7%
ValueCountFrequency (%)
2 17468
60.7%
1 6056
 
21.0%
0 77
 
0.3%

tce_imm_match
Text

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing11133
Missing (%)38.7%
Memory size225.1 KiB
2024-12-17T12:01:33.039875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53001
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP/P
2nd rowP/P
3rd rowP/P
4th rowP/P
5th rowP/P
ValueCountFrequency (%)
p/p 13114
74.2%
g/g 2522
 
14.3%
h/h 1084
 
6.1%
g/b 544
 
3.1%
h/b 229
 
1.3%
p/h 83
 
0.5%
p/b 66
 
0.4%
p/g 25
 
0.1%
2024-12-17T12:01:33.212887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 26402
49.8%
/ 17667
33.3%
G 5613
 
10.6%
H 2480
 
4.7%
B 839
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 26402
49.8%
/ 17667
33.3%
G 5613
 
10.6%
H 2480
 
4.7%
B 839
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 26402
49.8%
/ 17667
33.3%
G 5613
 
10.6%
H 2480
 
4.7%
B 839
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 26402
49.8%
/ 17667
33.3%
G 5613
 
10.6%
H 2480
 
4.7%
B 839
 
1.6%

hla_nmdp_6
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4197
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean5.160346299
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:33.310914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q36
95-th percentile6
Maximum6
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.203239838
Coefficient of variation (CV)0.2331703666
Kurtosis-0.6768503705
Mean5.160346299
Median Absolute Deviation (MAD)0
Skewness-1.014100122
Sum126960
Variance1.447786109
MonotonicityNot monotonic
2024-12-17T12:01:33.404401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
6 15105
52.4%
3 4888
 
17.0%
5 3296
 
11.4%
4 1279
 
4.4%
2 35
 
0.1%
(Missing) 4197
 
14.6%
ValueCountFrequency (%)
2 35
 
0.1%
3 4888
 
17.0%
4 1279
 
4.4%
5 3296
 
11.4%
6 15105
52.4%
ValueCountFrequency (%)
6 15105
52.4%
5 3296
 
11.4%
4 1279
 
4.4%
3 4888
 
17.0%
2 35
 
0.1%

hla_match_c_low
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2800
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1.757807692
Minimum0
Maximum2
Zeros79
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:33.495494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4354531087
Coefficient of variation (CV)0.2477251127
Kurtosis0.04577735309
Mean1.757807692
Median Absolute Deviation (MAD)0
Skewness-1.313564567
Sum45703
Variance0.1896194099
MonotonicityNot monotonic
2024-12-17T12:01:33.583986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 19782
68.7%
1 6139
 
21.3%
0 79
 
0.3%
(Missing) 2800
 
9.7%
ValueCountFrequency (%)
0 79
 
0.3%
1 6139
 
21.3%
2 19782
68.7%
ValueCountFrequency (%)
2 19782
68.7%
1 6139
 
21.3%
0 79
 
0.3%

rituximab
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2148
Missing (%)7.5%
Memory size225.1 KiB
2024-12-17T12:01:33.644146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.023225274
Min length2

Characters and Unicode

Total characters53923
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 26033
97.7%
yes 619
 
2.3%
2024-12-17T12:01:33.806898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 26033
48.3%
o 26033
48.3%
Y 619
 
1.1%
e 619
 
1.1%
s 619
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 26033
48.3%
o 26033
48.3%
Y 619
 
1.1%
e 619
 
1.1%
s 619
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 26033
48.3%
o 26033
48.3%
Y 619
 
1.1%
e 619
 
1.1%
s 619
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 26033
48.3%
o 26033
48.3%
Y 619
 
1.1%
e 619
 
1.1%
s 619
 
1.1%

hla_match_drb1_low
Real number (ℝ)

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2643
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean1.715296097
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:33.902973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4512819259
Coefficient of variation (CV)0.263092726
Kurtosis-1.089535325
Mean1.715296097
Median Absolute Deviation (MAD)0
Skewness-0.9542263821
Sum44867
Variance0.2036553767
MonotonicityNot monotonic
2024-12-17T12:01:33.989812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2 18710
65.0%
1 7447
 
25.9%
(Missing) 2643
 
9.2%
ValueCountFrequency (%)
1 7447
 
25.9%
2 18710
65.0%
ValueCountFrequency (%)
2 18710
65.0%
1 7447
 
25.9%

hla_match_dqb1_low
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4194
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean1.773795009
Minimum0
Maximum2
Zeros91
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:34.072343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4271302814
Coefficient of variation (CV)0.2408002498
Kurtosis0.5185631677
Mean1.773795009
Median Absolute Deviation (MAD)0
Skewness-1.450511156
Sum43646
Variance0.1824402773
MonotonicityNot monotonic
2024-12-17T12:01:34.161199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 19131
66.4%
1 5384
 
18.7%
0 91
 
0.3%
(Missing) 4194
 
14.6%
ValueCountFrequency (%)
0 91
 
0.3%
1 5384
 
18.7%
2 19131
66.4%
ValueCountFrequency (%)
2 19131
66.4%
1 5384
 
18.7%
0 91
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:34.230045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters57600
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBM
2nd rowPB
3rd rowBM
4th rowBM
5th rowPB
ValueCountFrequency (%)
pb 20381
70.8%
bm 8419
29.2%
2024-12-17T12:01:34.398933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 28800
50.0%
P 20381
35.4%
M 8419
 
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 28800
50.0%
P 20381
35.4%
M 8419
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 28800
50.0%
P 20381
35.4%
M 8419
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 28800
50.0%
P 20381
35.4%
M 8419
 
14.6%

cyto_score_detail
Text

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing11923
Missing (%)41.4%
Memory size225.1 KiB
2024-12-17T12:01:34.489904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length9.636724536
Min length3

Characters and Unicode

Total characters162639
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermediate
2nd rowIntermediate
3rd rowTBD
4th rowIntermediate
5th rowIntermediate
ValueCountFrequency (%)
intermediate 11158
65.6%
poor 3323
 
19.5%
favorable 1208
 
7.1%
tbd 1043
 
6.1%
not 145
 
0.9%
tested 145
 
0.9%
2024-12-17T12:01:34.675717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34972
21.5%
t 22751
14.0%
r 15689
9.6%
a 13574
 
8.3%
d 11303
 
6.9%
I 11158
 
6.9%
n 11158
 
6.9%
m 11158
 
6.9%
i 11158
 
6.9%
o 7999
 
4.9%
Other values (11) 11719
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34972
21.5%
t 22751
14.0%
r 15689
9.6%
a 13574
 
8.3%
d 11303
 
6.9%
I 11158
 
6.9%
n 11158
 
6.9%
m 11158
 
6.9%
i 11158
 
6.9%
o 7999
 
4.9%
Other values (11) 11719
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34972
21.5%
t 22751
14.0%
r 15689
9.6%
a 13574
 
8.3%
d 11303
 
6.9%
I 11158
 
6.9%
n 11158
 
6.9%
m 11158
 
6.9%
i 11158
 
6.9%
o 7999
 
4.9%
Other values (11) 11719
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34972
21.5%
t 22751
14.0%
r 15689
9.6%
a 13574
 
8.3%
d 11303
 
6.9%
I 11158
 
6.9%
n 11158
 
6.9%
m 11158
 
6.9%
i 11158
 
6.9%
o 7999
 
4.9%
Other values (11) 11719
 
7.2%
Distinct6
Distinct (%)< 0.1%
Missing4789
Missing (%)16.6%
Memory size225.1 KiB
2024-12-17T12:01:34.761900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length3
Mean length3.117862646
Min length3

Characters and Unicode

Total characters74863
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAC
2nd rowMAC
3rd rowMAC
4th rowMAC
5th rowRIC
ValueCountFrequency (%)
mac 12288
50.4%
ric 7722
31.7%
nma 3479
 
14.3%
tbd 373
 
1.5%
no 87
 
0.4%
drugs 87
 
0.4%
reported 87
 
0.4%
n/a 62
 
0.3%
f(pre-ted 62
 
0.3%
not 62
 
0.3%
2024-12-17T12:01:35.034755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 20010
26.7%
A 15829
21.1%
M 15767
21.1%
R 7722
 
10.3%
I 7722
 
10.3%
N 3628
 
4.8%
T 435
 
0.6%
D 435
 
0.6%
B 373
 
0.5%
360
 
0.5%
Other values (20) 2582
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 20010
26.7%
A 15829
21.1%
M 15767
21.1%
R 7722
 
10.3%
I 7722
 
10.3%
N 3628
 
4.8%
T 435
 
0.6%
D 435
 
0.6%
B 373
 
0.5%
360
 
0.5%
Other values (20) 2582
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 20010
26.7%
A 15829
21.1%
M 15767
21.1%
R 7722
 
10.3%
I 7722
 
10.3%
N 3628
 
4.8%
T 435
 
0.6%
D 435
 
0.6%
B 373
 
0.5%
360
 
0.5%
Other values (20) 2582
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 20010
26.7%
A 15829
21.1%
M 15767
21.1%
R 7722
 
10.3%
I 7722
 
10.3%
N 3628
 
4.8%
T 435
 
0.6%
D 435
 
0.6%
B 373
 
0.5%
360
 
0.5%
Other values (20) 2582
 
3.4%

ethnicity
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing587
Missing (%)2.0%
Memory size225.1 KiB
2024-12-17T12:01:35.129051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length21.55268848
Min length18

Characters and Unicode

Total characters608066
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Hispanic or Latino
2nd rowNot Hispanic or Latino
3rd rowNot Hispanic or Latino
4th rowNot Hispanic or Latino
5th rowHispanic or Latino
ValueCountFrequency (%)
hispanic 27829
25.4%
or 27829
25.4%
latino 27829
25.4%
not 24482
22.4%
non-resident 384
 
0.4%
of 384
 
0.4%
the 384
 
0.4%
u.s 384
 
0.4%
2024-12-17T12:01:35.321780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 83871
13.8%
81292
13.4%
o 80908
13.3%
n 56426
9.3%
a 55658
9.2%
t 53079
8.7%
s 28213
 
4.6%
r 28213
 
4.6%
c 27829
 
4.6%
L 27829
 
4.6%
Other values (11) 84748
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 83871
13.8%
81292
13.4%
o 80908
13.3%
n 56426
9.3%
a 55658
9.2%
t 53079
8.7%
s 28213
 
4.6%
r 28213
 
4.6%
c 27829
 
4.6%
L 27829
 
4.6%
Other values (11) 84748
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 83871
13.8%
81292
13.4%
o 80908
13.3%
n 56426
9.3%
a 55658
9.2%
t 53079
8.7%
s 28213
 
4.6%
r 28213
 
4.6%
c 27829
 
4.6%
L 27829
 
4.6%
Other values (11) 84748
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 83871
13.8%
81292
13.4%
o 80908
13.3%
n 56426
9.3%
a 55658
9.2%
t 53079
8.7%
s 28213
 
4.6%
r 28213
 
4.6%
c 27829
 
4.6%
L 27829
 
4.6%
Other values (11) 84748
13.9%

year_hct
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.179444
Minimum2008
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:35.423235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12013
median2016
Q32018
95-th percentile2018
Maximum2020
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.153913895
Coefficient of variation (CV)0.001565078437
Kurtosis0.1021579026
Mean2015.179444
Median Absolute Deviation (MAD)2
Skewness-1.093486973
Sum58037168
Variance9.947172857
MonotonicityNot monotonic
2024-12-17T12:01:35.522369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2018 7336
25.5%
2016 5049
17.5%
2017 4830
16.8%
2008 2544
 
8.8%
2015 2243
 
7.8%
2013 1871
 
6.5%
2012 1571
 
5.5%
2014 1098
 
3.8%
2019 774
 
2.7%
2011 599
 
2.1%
Other values (3) 885
 
3.1%
ValueCountFrequency (%)
2008 2544
8.8%
2009 503
 
1.7%
2010 378
 
1.3%
2011 599
 
2.1%
2012 1571
5.5%
ValueCountFrequency (%)
2020 4
 
< 0.1%
2019 774
 
2.7%
2018 7336
25.5%
2017 4830
16.8%
2016 5049
17.5%

obesity
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1760
Missing (%)6.1%
Memory size225.1 KiB
2024-12-17T12:01:35.586088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.091752959
Min length2

Characters and Unicode

Total characters56561
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 25144
92.6%
yes 1779
 
6.6%
not 117
 
0.4%
done 117
 
0.4%
2024-12-17T12:01:35.757120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 25378
44.9%
N 25261
44.7%
e 1896
 
3.4%
Y 1779
 
3.1%
s 1779
 
3.1%
t 117
 
0.2%
117
 
0.2%
d 117
 
0.2%
n 117
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25378
44.9%
N 25261
44.7%
e 1896
 
3.4%
Y 1779
 
3.1%
s 1779
 
3.1%
t 117
 
0.2%
117
 
0.2%
d 117
 
0.2%
n 117
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25378
44.9%
N 25261
44.7%
e 1896
 
3.4%
Y 1779
 
3.1%
s 1779
 
3.1%
t 117
 
0.2%
117
 
0.2%
d 117
 
0.2%
n 117
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25378
44.9%
N 25261
44.7%
e 1896
 
3.4%
Y 1779
 
3.1%
s 1779
 
3.1%
t 117
 
0.2%
117
 
0.2%
d 117
 
0.2%
n 117
 
0.2%

mrd_hct
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing16597
Missing (%)57.6%
Memory size225.1 KiB
2024-12-17T12:01:35.843106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters97624
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowNegative
4th rowPositive
5th rowNegative
ValueCountFrequency (%)
negative 8068
66.1%
positive 4135
33.9%
2024-12-17T12:01:36.021451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20271
20.8%
i 16338
16.7%
t 12203
12.5%
v 12203
12.5%
N 8068
 
8.3%
g 8068
 
8.3%
a 8068
 
8.3%
P 4135
 
4.2%
o 4135
 
4.2%
s 4135
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20271
20.8%
i 16338
16.7%
t 12203
12.5%
v 12203
12.5%
N 8068
 
8.3%
g 8068
 
8.3%
a 8068
 
8.3%
P 4135
 
4.2%
o 4135
 
4.2%
s 4135
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20271
20.8%
i 16338
16.7%
t 12203
12.5%
v 12203
12.5%
N 8068
 
8.3%
g 8068
 
8.3%
a 8068
 
8.3%
P 4135
 
4.2%
o 4135
 
4.2%
s 4135
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20271
20.8%
i 16338
16.7%
t 12203
12.5%
v 12203
12.5%
N 8068
 
8.3%
g 8068
 
8.3%
a 8068
 
8.3%
P 4135
 
4.2%
o 4135
 
4.2%
s 4135
 
4.2%
Distinct2
Distinct (%)< 0.1%
Missing225
Missing (%)0.8%
Memory size225.1 KiB
2024-12-17T12:01:36.097080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.384391951
Min length2

Characters and Unicode

Total characters68134
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes
ValueCountFrequency (%)
no 17591
61.6%
yes 10984
38.4%
2024-12-17T12:01:36.273071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 17591
25.8%
o 17591
25.8%
Y 10984
16.1%
e 10984
16.1%
s 10984
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 17591
25.8%
o 17591
25.8%
Y 10984
16.1%
e 10984
16.1%
s 10984
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 17591
25.8%
o 17591
25.8%
Y 10984
16.1%
e 10984
16.1%
s 10984
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 17591
25.8%
o 17591
25.8%
Y 10984
16.1%
e 10984
16.1%
s 10984
16.1%

tce_match
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing18996
Missing (%)66.0%
Memory size225.1 KiB
2024-12-17T12:01:36.364954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length10
Mean length12.34200326
Min length10

Characters and Unicode

Total characters121001
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPermissive
2nd rowPermissive
3rd rowPermissive
4th rowPermissive
5th rowPermissive
ValueCountFrequency (%)
permissive 6272
47.0%
non-permissive 2473
 
18.5%
gvh 1605
 
12.0%
fully 1059
 
7.9%
matched 1059
 
7.9%
hvg 868
 
6.5%
2024-12-17T12:01:36.566653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 18549
15.3%
i 17490
14.5%
s 17490
14.5%
v 11218
9.3%
m 9804
8.1%
r 8745
7.2%
P 6272
 
5.2%
n 4946
 
4.1%
3532
 
2.9%
p 2473
 
2.0%
Other values (13) 20482
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18549
15.3%
i 17490
14.5%
s 17490
14.5%
v 11218
9.3%
m 9804
8.1%
r 8745
7.2%
P 6272
 
5.2%
n 4946
 
4.1%
3532
 
2.9%
p 2473
 
2.0%
Other values (13) 20482
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18549
15.3%
i 17490
14.5%
s 17490
14.5%
v 11218
9.3%
m 9804
8.1%
r 8745
7.2%
P 6272
 
5.2%
n 4946
 
4.1%
3532
 
2.9%
p 2473
 
2.0%
Other values (13) 20482
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18549
15.3%
i 17490
14.5%
s 17490
14.5%
v 11218
9.3%
m 9804
8.1%
r 8745
7.2%
P 6272
 
5.2%
n 4946
 
4.1%
3532
 
2.9%
p 2473
 
2.0%
Other values (13) 20482
16.9%

hla_match_a_high
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4301
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean1.70374301
Minimum0
Maximum2
Zeros63
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:36.662213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4622126928
Coefficient of variation (CV)0.2712924954
Kurtosis-0.8414041314
Mean1.70374301
Median Absolute Deviation (MAD)0
Skewness-0.9704175186
Sum41740
Variance0.2136405734
MonotonicityNot monotonic
2024-12-17T12:01:36.752709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 17304
60.1%
1 7132
24.8%
0 63
 
0.2%
(Missing) 4301
 
14.9%
ValueCountFrequency (%)
0 63
 
0.2%
1 7132
24.8%
2 17304
60.1%
ValueCountFrequency (%)
2 17304
60.1%
1 7132
24.8%
0 63
 
0.2%

hepatic_severe
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1871
Missing (%)6.5%
Memory size225.1 KiB
2024-12-17T12:01:36.818979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.101786178
Min length2

Characters and Unicode

Total characters56599
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 25238
93.0%
yes 1481
 
5.5%
not 210
 
0.8%
done 210
 
0.8%
2024-12-17T12:01:36.989070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 25658
45.3%
N 25448
45.0%
e 1691
 
3.0%
Y 1481
 
2.6%
s 1481
 
2.6%
t 210
 
0.4%
210
 
0.4%
d 210
 
0.4%
n 210
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25658
45.3%
N 25448
45.0%
e 1691
 
3.0%
Y 1481
 
2.6%
s 1481
 
2.6%
t 210
 
0.4%
210
 
0.4%
d 210
 
0.4%
n 210
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25658
45.3%
N 25448
45.0%
e 1691
 
3.0%
Y 1481
 
2.6%
s 1481
 
2.6%
t 210
 
0.4%
210
 
0.4%
d 210
 
0.4%
n 210
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25658
45.3%
N 25448
45.0%
e 1691
 
3.0%
Y 1481
 
2.6%
s 1481
 
2.6%
t 210
 
0.4%
210
 
0.4%
d 210
 
0.4%
n 210
 
0.4%

donor_age
Real number (ℝ)

MISSING 

Distinct20909
Distinct (%)77.5%
Missing1808
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean42.51159084
Minimum18.01
Maximum84.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:37.103714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.01
5-th percentile22.33655
Q128.447
median40.063
Q356.1315
95-th percentile67.5913
Maximum84.8
Range66.79
Interquartile range (IQR)27.6845

Descriptive statistics

Standard deviation15.25143395
Coefficient of variation (CV)0.3587594264
Kurtosis-1.18412652
Mean42.51159084
Median Absolute Deviation (MAD)13.114
Skewness0.2962049646
Sum1147472.86
Variance232.6062374
MonotonicityNot monotonic
2024-12-17T12:01:37.222404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.57 7
 
< 0.1%
24.504 6
 
< 0.1%
25.378 6
 
< 0.1%
38.722 5
 
< 0.1%
36.313 5
 
< 0.1%
23.725 5
 
< 0.1%
25.185 5
 
< 0.1%
23.546 5
 
< 0.1%
23.718 5
 
< 0.1%
53.7 5
 
< 0.1%
Other values (20899) 26938
93.5%
(Missing) 1808
 
6.3%
ValueCountFrequency (%)
18.01 1
< 0.1%
18.012 1
< 0.1%
18.016 1
< 0.1%
18.02 2
< 0.1%
18.023 1
< 0.1%
ValueCountFrequency (%)
84.8 1
< 0.1%
82.509 1
< 0.1%
81.539 1
< 0.1%
81.503 1
< 0.1%
81.501 1
< 0.1%

prior_tumor
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1678
Missing (%)5.8%
Memory size225.1 KiB
2024-12-17T12:01:37.293630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.173991594
Min length2

Characters and Unicode

Total characters58963
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 23828
86.9%
yes 3009
 
11.0%
not 285
 
1.0%
done 285
 
1.0%
2024-12-17T12:01:37.464658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 24398
41.4%
N 24113
40.9%
e 3294
 
5.6%
Y 3009
 
5.1%
s 3009
 
5.1%
t 285
 
0.5%
285
 
0.5%
d 285
 
0.5%
n 285
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 24398
41.4%
N 24113
40.9%
e 3294
 
5.6%
Y 3009
 
5.1%
s 3009
 
5.1%
t 285
 
0.5%
285
 
0.5%
d 285
 
0.5%
n 285
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 24398
41.4%
N 24113
40.9%
e 3294
 
5.6%
Y 3009
 
5.1%
s 3009
 
5.1%
t 285
 
0.5%
285
 
0.5%
d 285
 
0.5%
n 285
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 24398
41.4%
N 24113
40.9%
e 3294
 
5.6%
Y 3009
 
5.1%
s 3009
 
5.1%
t 285
 
0.5%
285
 
0.5%
d 285
 
0.5%
n 285
 
0.5%

hla_match_b_low
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2565
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1.719916143
Minimum0
Maximum2
Zeros64
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:37.560007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4544488319
Coefficient of variation (CV)0.2642273194
Kurtosis-0.6618341693
Mean1.719916143
Median Absolute Deviation (MAD)0
Skewness-1.057329765
Sum45122
Variance0.2065237408
MonotonicityNot monotonic
2024-12-17T12:01:37.651383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 18951
65.8%
1 7220
 
25.1%
0 64
 
0.2%
(Missing) 2565
 
8.9%
ValueCountFrequency (%)
0 64
 
0.2%
1 7220
 
25.1%
2 18951
65.8%
ValueCountFrequency (%)
2 18951
65.8%
1 7220
 
25.1%
0 64
 
0.2%

peptic_ulcer
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2419
Missing (%)8.4%
Memory size225.1 KiB
2024-12-17T12:01:37.717476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.047572116
Min length2

Characters and Unicode

Total characters54017
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 25956
97.8%
yes 259
 
1.0%
not 166
 
0.6%
done 166
 
0.6%
2024-12-17T12:01:37.886499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 26288
48.7%
N 26122
48.4%
e 425
 
0.8%
Y 259
 
0.5%
s 259
 
0.5%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26288
48.7%
N 26122
48.4%
e 425
 
0.8%
Y 259
 
0.5%
s 259
 
0.5%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26288
48.7%
N 26122
48.4%
e 425
 
0.8%
Y 259
 
0.5%
s 259
 
0.5%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26288
48.7%
N 26122
48.4%
e 425
 
0.8%
Y 259
 
0.5%
s 259
 
0.5%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

age_at_hct
Real number (ℝ)

Distinct22168
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.66316177
Minimum0.044
Maximum73.726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:38.002625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.044
5-th percentile0.96185
Q119.539
median41.006
Q355.96525
95-th percentile66.33815
Maximum73.726
Range73.682
Interquartile range (IQR)36.42625

Descriptive statistics

Standard deviation21.14758078
Coefficient of variation (CV)0.5469697719
Kurtosis-1.064923699
Mean38.66316177
Median Absolute Deviation (MAD)16.496
Skewness-0.4038133453
Sum1113499.059
Variance447.2201726
MonotonicityNot monotonic
2024-12-17T12:01:38.122799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 1005
 
3.5%
64.47 6
 
< 0.1%
15.82 6
 
< 0.1%
63.701 5
 
< 0.1%
65.184 5
 
< 0.1%
63.217 5
 
< 0.1%
50.613 5
 
< 0.1%
63.818 5
 
< 0.1%
54.496 5
 
< 0.1%
37.722 5
 
< 0.1%
Other values (22158) 27748
96.3%
ValueCountFrequency (%)
0.044 1005
3.5%
0.046 1
 
< 0.1%
0.05 1
 
< 0.1%
0.053 1
 
< 0.1%
0.057 1
 
< 0.1%
ValueCountFrequency (%)
73.726 1
< 0.1%
73.717 1
< 0.1%
73.67 1
< 0.1%
73.574 1
< 0.1%
73.459 1
< 0.1%

hla_match_a_low
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2390
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean1.709087467
Minimum0
Maximum2
Zeros49
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:38.219755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.458258768
Coefficient of variation (CV)0.2681306702
Kurtosis-0.8789295431
Mean1.709087467
Median Absolute Deviation (MAD)0
Skewness-0.9785073653
Sum45137
Variance0.2100010984
MonotonicityNot monotonic
2024-12-17T12:01:38.311134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 18776
65.2%
1 7585
26.3%
0 49
 
0.2%
(Missing) 2390
 
8.3%
ValueCountFrequency (%)
0 49
 
0.2%
1 7585
26.3%
2 18776
65.2%
ValueCountFrequency (%)
2 18776
65.2%
1 7585
26.3%
0 49
 
0.2%
Distinct17
Distinct (%)0.1%
Missing225
Missing (%)0.8%
Memory size225.1 KiB
2024-12-17T12:01:38.401763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length20.56787402
Min length7

Characters and Unicode

Total characters587727
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFKalone
2nd rowOther GVHD Prophylaxis
3rd rowCyclophosphamide alone
4th rowFK+ MMF +- others
5th rowTDEPLETION +- other
ValueCountFrequency (%)
22754
21.9%
fk 16981
16.4%
mmf 16980
16.4%
others 12809
12.3%
cyclophosphamide 7639
 
7.4%
others(not 6788
 
6.5%
alone 6280
 
6.1%
mtx 4486
 
4.3%
csa 2739
 
2.6%
fkalone 1230
 
1.2%
Other values (14) 5040
 
4.9%
2024-12-17T12:01:38.596898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75151
 
12.8%
o 50903
 
8.7%
M 38966
 
6.6%
+ 37395
 
6.4%
h 36831
 
6.3%
e 36688
 
6.2%
F 35686
 
6.1%
s 28416
 
4.8%
t 28021
 
4.8%
r 21615
 
3.7%
Other values (36) 198055
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
75151
 
12.8%
o 50903
 
8.7%
M 38966
 
6.6%
+ 37395
 
6.4%
h 36831
 
6.3%
e 36688
 
6.2%
F 35686
 
6.1%
s 28416
 
4.8%
t 28021
 
4.8%
r 21615
 
3.7%
Other values (36) 198055
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
75151
 
12.8%
o 50903
 
8.7%
M 38966
 
6.6%
+ 37395
 
6.4%
h 36831
 
6.3%
e 36688
 
6.2%
F 35686
 
6.1%
s 28416
 
4.8%
t 28021
 
4.8%
r 21615
 
3.7%
Other values (36) 198055
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
75151
 
12.8%
o 50903
 
8.7%
M 38966
 
6.6%
+ 37395
 
6.4%
h 36831
 
6.3%
e 36688
 
6.2%
F 35686
 
6.1%
s 28416
 
4.8%
t 28021
 
4.8%
r 21615
 
3.7%
Other values (36) 198055
33.7%

rheum_issue
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2183
Missing (%)7.6%
Memory size225.1 KiB
2024-12-17T12:01:38.669079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.049855356
Min length2

Characters and Unicode

Total characters54561
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 26015
97.2%
yes 457
 
1.7%
not 145
 
0.5%
done 145
 
0.5%
2024-12-17T12:01:38.839341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 26305
48.2%
N 26160
47.9%
e 602
 
1.1%
Y 457
 
0.8%
s 457
 
0.8%
t 145
 
0.3%
145
 
0.3%
d 145
 
0.3%
n 145
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26305
48.2%
N 26160
47.9%
e 602
 
1.1%
Y 457
 
0.8%
s 457
 
0.8%
t 145
 
0.3%
145
 
0.3%
d 145
 
0.3%
n 145
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26305
48.2%
N 26160
47.9%
e 602
 
1.1%
Y 457
 
0.8%
s 457
 
0.8%
t 145
 
0.3%
145
 
0.3%
d 145
 
0.3%
n 145
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26305
48.2%
N 26160
47.9%
e 602
 
1.1%
Y 457
 
0.8%
s 457
 
0.8%
t 145
 
0.3%
145
 
0.3%
d 145
 
0.3%
n 145
 
0.3%
Distinct4
Distinct (%)< 0.1%
Missing261
Missing (%)0.9%
Memory size225.1 KiB
2024-12-17T12:01:38.927430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters85617
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM-F
2nd rowF-F
3rd rowF-M
4th rowM-M
5th rowM-F
ValueCountFrequency (%)
m-m 7980
28.0%
f-m 7822
27.4%
m-f 6715
23.5%
f-f 6022
21.1%
2024-12-17T12:01:39.104038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 30497
35.6%
- 28539
33.3%
F 26581
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 30497
35.6%
- 28539
33.3%
F 26581
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 30497
35.6%
- 28539
33.3%
F 26581
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 30497
35.6%
- 28539
33.3%
F 26581
31.0%

hla_match_b_high
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4088
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean1.699619618
Minimum0
Maximum2
Zeros77
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:39.199454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4651801317
Coefficient of variation (CV)0.2736966123
Kurtosis-0.8173189096
Mean1.699619618
Median Absolute Deviation (MAD)0
Skewness-0.9635494513
Sum42001
Variance0.2163925549
MonotonicityNot monotonic
2024-12-17T12:01:39.291391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 17366
60.3%
1 7269
25.2%
0 77
 
0.3%
(Missing) 4088
 
14.2%
ValueCountFrequency (%)
0 77
 
0.3%
1 7269
25.2%
2 17366
60.3%
ValueCountFrequency (%)
2 17366
60.3%
1 7269
25.2%
0 77
 
0.3%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:39.387996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length32
Mean length20.89121528
Min length5

Characters and Unicode

Total characters601667
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMore than one race
2nd rowAsian
3rd rowMore than one race
4th rowWhite
5th rowAmerican Indian or Alaska Native
ValueCountFrequency (%)
or 14292
14.9%
native 9497
 
9.9%
more 4845
 
5.1%
than 4845
 
5.1%
one 4845
 
5.1%
race 4845
 
5.1%
asian 4832
 
5.1%
white 4831
 
5.1%
african-american 4795
 
5.0%
black 4795
 
5.0%
Other values (7) 33198
34.7%
2024-12-17T12:01:39.597920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 81099
13.5%
66820
11.1%
i 57158
 
9.5%
n 47896
 
8.0%
e 47862
 
8.0%
r 47776
 
7.9%
c 33434
 
5.6%
o 28689
 
4.8%
A 24002
 
4.0%
t 23880
 
4.0%
Other values (17) 143051
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 601667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 81099
13.5%
66820
11.1%
i 57158
 
9.5%
n 47896
 
8.0%
e 47862
 
8.0%
r 47776
 
7.9%
c 33434
 
5.6%
o 28689
 
4.8%
A 24002
 
4.0%
t 23880
 
4.0%
Other values (17) 143051
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 601667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 81099
13.5%
66820
11.1%
i 57158
 
9.5%
n 47896
 
8.0%
e 47862
 
8.0%
r 47776
 
7.9%
c 33434
 
5.6%
o 28689
 
4.8%
A 24002
 
4.0%
t 23880
 
4.0%
Other values (17) 143051
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 601667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 81099
13.5%
66820
11.1%
i 57158
 
9.5%
n 47896
 
8.0%
e 47862
 
8.0%
r 47776
 
7.9%
c 33434
 
5.6%
o 28689
 
4.8%
A 24002
 
4.0%
t 23880
 
4.0%
Other values (17) 143051
23.8%

comorbidity_score
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing477
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean1.702326731
Minimum0
Maximum10
Zeros10738
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:39.801663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.994442854
Coefficient of variation (CV)1.171598153
Kurtosis2.067382354
Mean1.702326731
Median Absolute Deviation (MAD)1
Skewness1.474656976
Sum48215
Variance3.977802297
MonotonicityNot monotonic
2024-12-17T12:01:39.890311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 10738
37.3%
2 5899
20.5%
1 4852
16.8%
3 2460
 
8.5%
4 1396
 
4.8%
5 1219
 
4.2%
6 708
 
2.5%
7 492
 
1.7%
8 293
 
1.0%
9 190
 
0.7%
(Missing) 477
 
1.7%
ValueCountFrequency (%)
0 10738
37.3%
1 4852
16.8%
2 5899
20.5%
3 2460
 
8.5%
4 1396
 
4.8%
ValueCountFrequency (%)
10 76
 
0.3%
9 190
 
0.7%
8 293
1.0%
7 492
1.7%
6 708
2.5%

karnofsky_score
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing870
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean83.8320802
Minimum40
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:39.971546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile70
Q170
median90
Q390
95-th percentile100
Maximum100
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.02883973
Coefficient of variation (CV)0.1315587029
Kurtosis-0.5572129559
Mean83.8320802
Median Absolute Deviation (MAD)0
Skewness-0.6832485257
Sum2341430
Variance121.6353058
MonotonicityNot monotonic
2024-12-17T12:01:40.056466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
90 15336
53.2%
70 6690
23.2%
100 2476
 
8.6%
80 2036
 
7.1%
60 1291
 
4.5%
50 91
 
0.3%
40 10
 
< 0.1%
(Missing) 870
 
3.0%
ValueCountFrequency (%)
40 10
 
< 0.1%
50 91
 
0.3%
60 1291
 
4.5%
70 6690
23.2%
80 2036
 
7.1%
ValueCountFrequency (%)
100 2476
 
8.6%
90 15336
53.2%
80 2036
 
7.1%
70 6690
23.2%
60 1291
 
4.5%

hepatic_mild
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1917
Missing (%)6.7%
Memory size225.1 KiB
2024-12-17T12:01:40.118488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.096492207
Min length2

Characters and Unicode

Total characters56360
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo
ValueCountFrequency (%)
no 24989
92.5%
yes 1754
 
6.5%
not 140
 
0.5%
done 140
 
0.5%
2024-12-17T12:01:40.308451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 25269
44.8%
N 25129
44.6%
e 1894
 
3.4%
Y 1754
 
3.1%
s 1754
 
3.1%
t 140
 
0.2%
140
 
0.2%
d 140
 
0.2%
n 140
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25269
44.8%
N 25129
44.6%
e 1894
 
3.4%
Y 1754
 
3.1%
s 1754
 
3.1%
t 140
 
0.2%
140
 
0.2%
d 140
 
0.2%
n 140
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25269
44.8%
N 25129
44.6%
e 1894
 
3.4%
Y 1754
 
3.1%
s 1754
 
3.1%
t 140
 
0.2%
140
 
0.2%
d 140
 
0.2%
n 140
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25269
44.8%
N 25129
44.6%
e 1894
 
3.4%
Y 1754
 
3.1%
s 1754
 
3.1%
t 140
 
0.2%
140
 
0.2%
d 140
 
0.2%
n 140
 
0.2%

tce_div_match
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing11396
Missing (%)39.6%
Memory size225.1 KiB
2024-12-17T12:01:40.414963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length21
Mean length20.60463112
Min length18

Characters and Unicode

Total characters358603
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPermissive mismatched
2nd rowPermissive mismatched
3rd rowPermissive mismatched
4th rowPermissive mismatched
5th rowPermissive mismatched
ValueCountFrequency (%)
permissive 12936
37.2%
mismatched 12936
37.2%
non-permissive 4468
 
12.8%
gvh 2458
 
7.1%
hvg 1417
 
4.1%
bi-directional 593
 
1.7%
2024-12-17T12:01:40.636923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 49523
13.8%
e 48337
13.5%
s 47744
13.3%
m 43276
12.1%
v 21279
 
5.9%
r 17997
 
5.0%
17404
 
4.9%
c 13529
 
3.8%
d 13529
 
3.8%
a 13529
 
3.8%
Other values (11) 72456
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 49523
13.8%
e 48337
13.5%
s 47744
13.3%
m 43276
12.1%
v 21279
 
5.9%
r 17997
 
5.0%
17404
 
4.9%
c 13529
 
3.8%
d 13529
 
3.8%
a 13529
 
3.8%
Other values (11) 72456
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 49523
13.8%
e 48337
13.5%
s 47744
13.3%
m 43276
12.1%
v 21279
 
5.9%
r 17997
 
5.0%
17404
 
4.9%
c 13529
 
3.8%
d 13529
 
3.8%
a 13529
 
3.8%
Other values (11) 72456
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 49523
13.8%
e 48337
13.5%
s 47744
13.3%
m 43276
12.1%
v 21279
 
5.9%
r 17997
 
5.0%
17404
 
4.9%
c 13529
 
3.8%
d 13529
 
3.8%
a 13529
 
3.8%
Other values (11) 72456
20.2%
Distinct3
Distinct (%)< 0.1%
Missing158
Missing (%)0.5%
Memory size225.1 KiB
2024-12-17T12:01:40.735526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length7
Mean length8.049437888
Min length7

Characters and Unicode

Total characters230552
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnrelated
2nd rowRelated
3rd rowRelated
4th rowUnrelated
5th rowRelated
ValueCountFrequency (%)
related 16208
55.3%
unrelated 12088
41.2%
multiple 346
 
1.2%
donor 346
 
1.2%
non-ucb 346
 
1.2%
2024-12-17T12:01:40.943955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 56938
24.7%
l 28988
12.6%
t 28642
12.4%
d 28642
12.4%
a 28296
12.3%
R 16208
 
7.0%
n 13126
 
5.7%
U 12434
 
5.4%
r 12434
 
5.4%
o 1038
 
0.5%
Other values (10) 3806
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 56938
24.7%
l 28988
12.6%
t 28642
12.4%
d 28642
12.4%
a 28296
12.3%
R 16208
 
7.0%
n 13126
 
5.7%
U 12434
 
5.4%
r 12434
 
5.4%
o 1038
 
0.5%
Other values (10) 3806
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 56938
24.7%
l 28988
12.6%
t 28642
12.4%
d 28642
12.4%
a 28296
12.3%
R 16208
 
7.0%
n 13126
 
5.7%
U 12434
 
5.4%
r 12434
 
5.4%
o 1038
 
0.5%
Other values (10) 3806
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 56938
24.7%
l 28988
12.6%
t 28642
12.4%
d 28642
12.4%
a 28296
12.3%
R 16208
 
7.0%
n 13126
 
5.7%
U 12434
 
5.4%
r 12434
 
5.4%
o 1038
 
0.5%
Other values (10) 3806
 
1.7%

melphalan_dose
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1405
Missing (%)4.9%
Memory size225.1 KiB
2024-12-17T12:01:41.039083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length14.02482205
Min length3

Characters and Unicode

Total characters384210
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN/A, Mel not given
2nd rowN/A, Mel not given
3rd rowN/A, Mel not given
4th rowN/A, Mel not given
5th rowMEL
ValueCountFrequency (%)
mel 27395
31.2%
n/a 20135
22.9%
not 20135
22.9%
given 20135
22.9%
2024-12-17T12:01:41.249999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60405
15.7%
e 40270
10.5%
n 40270
10.5%
M 27395
 
7.1%
N 20135
 
5.2%
/ 20135
 
5.2%
A 20135
 
5.2%
, 20135
 
5.2%
l 20135
 
5.2%
o 20135
 
5.2%
Other values (6) 95060
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 384210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60405
15.7%
e 40270
10.5%
n 40270
10.5%
M 27395
 
7.1%
N 20135
 
5.2%
/ 20135
 
5.2%
A 20135
 
5.2%
, 20135
 
5.2%
l 20135
 
5.2%
o 20135
 
5.2%
Other values (6) 95060
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 384210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60405
15.7%
e 40270
10.5%
n 40270
10.5%
M 27395
 
7.1%
N 20135
 
5.2%
/ 20135
 
5.2%
A 20135
 
5.2%
, 20135
 
5.2%
l 20135
 
5.2%
o 20135
 
5.2%
Other values (6) 95060
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 384210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60405
15.7%
e 40270
10.5%
n 40270
10.5%
M 27395
 
7.1%
N 20135
 
5.2%
/ 20135
 
5.2%
A 20135
 
5.2%
, 20135
 
5.2%
l 20135
 
5.2%
o 20135
 
5.2%
Other values (6) 95060
24.7%

hla_low_res_8
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing3653
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean6.903447727
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:41.353552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median8
Q38
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.565017269
Coefficient of variation (CV)0.2267008213
Kurtosis-0.6619286986
Mean6.903447727
Median Absolute Deviation (MAD)0
Skewness-1.016377176
Sum173601
Variance2.449279054
MonotonicityNot monotonic
2024-12-17T12:01:41.450031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 15160
52.6%
4 4259
 
14.8%
7 2603
 
9.0%
5 1613
 
5.6%
6 1488
 
5.2%
3 23
 
0.1%
2 1
 
< 0.1%
(Missing) 3653
 
12.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 23
 
0.1%
4 4259
14.8%
5 1613
 
5.6%
6 1488
 
5.2%
ValueCountFrequency (%)
8 15160
52.6%
7 2603
 
9.0%
6 1488
 
5.2%
5 1613
 
5.6%
4 4259
 
14.8%

cardiac
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2542
Missing (%)8.8%
Memory size225.1 KiB
2024-12-17T12:01:41.512440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.0914388
Min length2

Characters and Unicode

Total characters54917
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 24592
93.1%
yes 1519
 
5.8%
not 147
 
0.6%
done 147
 
0.6%
2024-12-17T12:01:41.684208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 24886
45.3%
N 24739
45.0%
e 1666
 
3.0%
Y 1519
 
2.8%
s 1519
 
2.8%
t 147
 
0.3%
147
 
0.3%
d 147
 
0.3%
n 147
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 24886
45.3%
N 24739
45.0%
e 1666
 
3.0%
Y 1519
 
2.8%
s 1519
 
2.8%
t 147
 
0.3%
147
 
0.3%
d 147
 
0.3%
n 147
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 24886
45.3%
N 24739
45.0%
e 1666
 
3.0%
Y 1519
 
2.8%
s 1519
 
2.8%
t 147
 
0.3%
147
 
0.3%
d 147
 
0.3%
n 147
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 24886
45.3%
N 24739
45.0%
e 1666
 
3.0%
Y 1519
 
2.8%
s 1519
 
2.8%
t 147
 
0.3%
147
 
0.3%
d 147
 
0.3%
n 147
 
0.3%

hla_match_drb1_high
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing3352
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean1.707128262
Minimum0
Maximum2
Zeros71
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:41.781201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4611791921
Coefficient of variation (CV)0.2701491168
Kurtosis-0.7724977502
Mean1.707128262
Median Absolute Deviation (MAD)0
Skewness-0.9954532784
Sum43443
Variance0.2126862472
MonotonicityNot monotonic
2024-12-17T12:01:41.873139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2 18066
62.7%
1 7311
25.4%
0 71
 
0.2%
(Missing) 3352
 
11.6%
ValueCountFrequency (%)
0 71
 
0.2%
1 7311
25.4%
2 18066
62.7%
ValueCountFrequency (%)
2 18066
62.7%
1 7311
25.4%
0 71
 
0.2%

pulm_moderate
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2047
Missing (%)7.1%
Memory size225.1 KiB
2024-12-17T12:01:41.942042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.233431765
Min length2

Characters and Unicode

Total characters59751
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 21338
79.3%
yes 5249
 
19.5%
not 166
 
0.6%
done 166
 
0.6%
2024-12-17T12:01:42.116965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 21670
36.3%
N 21504
36.0%
e 5415
 
9.1%
Y 5249
 
8.8%
s 5249
 
8.8%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 21670
36.3%
N 21504
36.0%
e 5415
 
9.1%
Y 5249
 
8.8%
s 5249
 
8.8%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 21670
36.3%
N 21504
36.0%
e 5415
 
9.1%
Y 5249
 
8.8%
s 5249
 
8.8%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 21670
36.3%
N 21504
36.0%
e 5415
 
9.1%
Y 5249
 
8.8%
s 5249
 
8.8%
t 166
 
0.3%
166
 
0.3%
d 166
 
0.3%
n 166
 
0.3%

hla_low_res_10
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing5064
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean8.664686552
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:42.212827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median10
Q310
95-th percentile10
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.882746161
Coefficient of variation (CV)0.2172895868
Kurtosis-0.5577034924
Mean8.664686552
Median Absolute Deviation (MAD)0
Skewness-1.042780659
Sum205665
Variance3.544733106
MonotonicityNot monotonic
2024-12-17T12:01:42.299728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 13734
47.7%
5 3211
 
11.1%
9 2544
 
8.8%
6 1664
 
5.8%
8 1387
 
4.8%
7 1170
 
4.1%
4 26
 
0.1%
(Missing) 5064
 
17.6%
ValueCountFrequency (%)
4 26
 
0.1%
5 3211
11.1%
6 1664
5.8%
7 1170
 
4.1%
8 1387
4.8%
ValueCountFrequency (%)
10 13734
47.7%
9 2544
 
8.8%
8 1387
 
4.8%
7 1170
 
4.1%
6 1664
 
5.8%

efs
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5393055556
Minimum0
Maximum1
Zeros13268
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:42.388454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.498461333
Coefficient of variation (CV)0.9242651552
Kurtosis-1.975262053
Mean0.5393055556
Median Absolute Deviation (MAD)0
Skewness-0.1577184937
Sum15532
Variance0.2484637005
MonotonicityNot monotonic
2024-12-17T12:01:42.474431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 15532
53.9%
0 13268
46.1%
ValueCountFrequency (%)
0 13268
46.1%
1 15532
53.9%
ValueCountFrequency (%)
1 15532
53.9%
0 13268
46.1%

efs_time
Real number (ℝ)

Distinct19208
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.23767816
Minimum0.333
Maximum156.819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.1 KiB
2024-12-17T12:01:42.577858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.333
5-th percentile3.31695
Q15.61975
median9.7965
Q335.1
95-th percentile74.5866
Maximum156.819
Range156.486
Interquartile range (IQR)29.48025

Descriptive statistics

Standard deviation24.79974839
Coefficient of variation (CV)1.067221442
Kurtosis3.063962041
Mean23.23767816
Median Absolute Deviation (MAD)6.2955
Skewness1.700399161
Sum669245.131
Variance615.0275203
MonotonicityNot monotonic
2024-12-17T12:01:42.696259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.697 10
 
< 0.1%
5.643 10
 
< 0.1%
5.801 9
 
< 0.1%
5.608 9
 
< 0.1%
5.886 9
 
< 0.1%
5.089 8
 
< 0.1%
4.727 8
 
< 0.1%
5.244 8
 
< 0.1%
5.033 8
 
< 0.1%
4.716 8
 
< 0.1%
Other values (19198) 28713
99.7%
ValueCountFrequency (%)
0.333 1
< 0.1%
0.482 1
< 0.1%
0.523 1
< 0.1%
0.533 1
< 0.1%
0.543 1
< 0.1%
ValueCountFrequency (%)
156.819 1
< 0.1%
155.983 1
< 0.1%
155.283 1
< 0.1%
154.249 1
< 0.1%
153.711 1
< 0.1%